DocumentCode :
296929
Title :
An approach to fault diagnosis in dynamic systems using Kohonen neural networks
Author :
Ficola, Antonio ; Cava, Michele La ; Magnino, Fabio
Author_Institution :
Istituto di Elettronica, Perugia Univ., Italy
Volume :
1
fYear :
34881
fDate :
10-14 Jul1995
Firstpage :
166
Abstract :
In this paper, a fault diagnosis system for linear dynamic systems based on Kohonen neural networks is proposed. The technique of pattern recognition is taken into account for the classification of the modes of operation of the system. The pattern is given by the coefficients of the transfer matrix which are estimated by a least squares algorithm; in this way classification can also be achieved under dynamic conditions. The method employs an unsupervised neural network based on competitive learning. An example is proposed to show the effectiveness of this approach
Keywords :
fault diagnosis; least squares approximations; linear systems; pattern classification; self-organising feature maps; transfer function matrices; unsupervised learning; Kohonen neural networks; competitive learning; fault diagnosis; least squares algorithm; linear dynamic systems; operation mode classification; pattern recognition; transfer matrix coefficients; unsupervised neural network; Electronic mail; Fault detection; Fault diagnosis; Feature extraction; Intelligent networks; Least squares approximation; Neural networks; Parameter estimation; Pattern recognition; State estimation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Electronics, 1995. ISIE '95., Proceedings of the IEEE International Symposium on
Conference_Location :
Athens
Print_ISBN :
0-7803-7369-3
Type :
conf
DOI :
10.1109/ISIE.1995.496495
Filename :
496495
Link To Document :
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